Micro-Expression Spotting: A Benchmark

Hong, Xiaopeng; Tran, Thuong-Khanh; Zhao, Guoying

doi:10.48550/arxiv.1710.02820

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Xia [13] applied machine learning to microexpression spotting and considered the relationship between frames and used adaboost to predict the probability of a certain frame as a micro-expression. Hong [14] used a sliding window to detect micro-expressions in samples with a fixed number of frames and treated micro-expression spotting as a binary classification task. Nag [15] proposed a joint architecture of temporal and spatial information to detect the onset frame and offset frame of microexpressions.…”

Section: Related Workmentioning

confidence: 99%

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Yang,

Wu,

2023

Electronics

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Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medicine. However, because of their characteristics such as short duration, low intensity, and sparse action units, this makes micro-expression spotting difficult. To address this problem and inspired by object detection methods, we propose a VoVNet-based micro-expression spotting model, driven by multi-scale features. Firstly, VoVNet is used to achieve the extraction and reuse of different scale perceptual field features to improve the feature extraction capability. Secondly, multi-scale features are extracted and fused using the Feature Pyramid Network module, incorporating optical flow features, and by realizing the interactive fusion of fine-grained feature information and semantic feature information. Finally, the model is trained and optimized on CAS(ME)2 and SAMM Long Video. The experimental results show that the F1 score of the proposed model is improved by 0.1963 and 0.2441 on the two datasets compared with the baseline method, which outperforms the most popular spotting methods.

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Section: Related Workmentioning

confidence: 99%

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Yang,

Wu,

2023

Electronics

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“…Promising progresses have been made over the past decade, in aspects such as dataset construction [2][3][4] , spatialtemporal descriptors either by hand-crafting [5][6][7][8][9][10][11][12] , or deep learning [13][14][15][16][17] , 18,19 , etc. However, it is still far away from satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework

Peng

Hong

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facial expressions. Automatic MER is challenging because that 1) the intensity of subtle facial muscle movement is extremely low and 2) the duration of ME is transient. Recent works adopt motion magnification or time interpolation to resolve these issues. Nevertheless, existing works divide them into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, it ignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, we explore their underlying joint formulations and propose a consolidated Eulerian framework to reveal the subtle facial movements. It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared to existing approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movements more distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art in both speed and accuracy.

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An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

Xie¹,

Lo²,

Shuai³

et al. 2023

IEEE Trans. Affective Comput.

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Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation.

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Micro-Expression Spotting: A Benchmark

Cited by 3 publications

References 48 publications

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework

An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

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